Result and analysis: TABLE REF  shows the result of ConAM on ResNet-18 [1]}, ResNet-34 [1]}, ResNet-50 [1]}, and ResNet-101 [1]} with different datasets, normalization functions, patch sizes, and learning rates. We can infer from the results that the accuracy of the proposed ConAM on all models and dataset reaches the maximum value. The combination of Softmax and [8, 8, 4, 0] achieves the best performance on CIFAR-100 [5]} for all models and learning rates. For CIFAR-10 [5]}, the combination of Sigmoid and [8 ,8, 4, 0] can achieve the best effect on almost all models. Especially for ResNet-18 [1]}, our ConAM with almost all patch sizes and normalization functions surpasses the baseline. As for ResNet-50 [1]} with \(\rm Learning~rate_1\) , the performance of ConAM with almost all patch sizes and normalization functions on CIFAR-100 [5]} outperforms the baseline. The performance of ResNet-101 [1]} enhanced by ConAM on CIFAR-10 [5]} almost completely exceeds the original model. In other models, ConAM also achieves competitive results. In the verification results of ResNet-34 [1]} on CIFAR-100 [5]} and ResNet-50 [1]} on CIFAR-10 [5]}, ConAM obtains better performance than the original model.
<TABLE>